one noise variable, logistic regression
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## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 33"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8068 -1.0493 0.5770 0.9415 2.5190
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18447 0.05074 3.635 0.000277 ***
## n1 2.20269 0.13545 16.262 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2256.7 on 1998 degrees of freedom
## AIC: 2260.7
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"
## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"
## [1] "effects model, sigma= 33"
## [1] "one noise variable, logistic regression effects model, sigma= 33 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.253 -1.196 1.102 1.148 1.427
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.04177 0.04621 0.904 0.365995
## n1 0.20597 0.05882 3.502 0.000463 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2760.1 on 1998 degrees of freedom
## AIC: 2764.1
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one noise variable, logistic regression Noised 33 train mean deviance 1.99098170430528"
## [1] "one noise variable, logistic regression Noised 33 test mean deviance 2.00767495136368"
## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3619 -1.1570 0.9662 1.1980 1.2169
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04838 0.04731 -1.023 0.30650
## n1 -0.06366 0.01954 -3.258 0.00112 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2761.8 on 1998 degrees of freedom
## AIC: 2765.8
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"
## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"
## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.999 2.000 2.001 2.001 2.001 2.006
## [1] 0.0009731397
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## [1] "one noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.999 2.001 2.002 2.003 2.005 2.022
## [1] 0.003580925
## [1] "********"
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## [1] "one noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.424 3.855 3.956 3.980 4.094 4.529
## [1] 0.2145439
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.999 2.002 2.003 2.005 2.008 2.023
## [1] 0.004262642
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.999 2.000 2.000 2.000 2.001 2.006
## [1] 0.0008414625
## [1] "********"
## [1] "*************************************************************"
one variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 5"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1243 -1.1809 0.4704 1.1554 1.5778
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4731 0.0542 8.73 <2e-16 ***
## x1 3.1777 0.2114 15.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2434.7 on 1998 degrees of freedom
## AIC: 2438.7
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"
## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"
## [1] "effects model, sigma= 5"
## [1] "one variable, logistic regression effects model, sigma= 5 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0770 -1.1737 0.4958 1.1624 1.6188
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4388 0.0527 8.326 <2e-16 ***
## x1 3.1337 0.2079 15.073 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2444.1 on 1998 degrees of freedom
## AIC: 2448.1
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression Noised 5 train mean deviance 1.76306565820564"
## [1] "one variable, logistic regression Noised 5 test mean deviance 1.75523069171642"
## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0811 -1.1892 0.4966 1.1600 1.5642
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.45308 0.05326 8.508 <2e-16 ***
## x1 2.99703 0.20478 14.636 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2460.2 on 1998 degrees of freedom
## AIC: 2464.2
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"
## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"
## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.736 1.759 1.771 1.770 1.780 1.801
## [1] 0.01361791
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## [1] "one variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.738 1.760 1.770 1.771 1.781 1.803
## [1] 0.01355715
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## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.737 1.761 1.772 1.771 1.783 1.805
## [1] 0.01425687
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## [1] "one variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.739 1.760 1.772 1.774 1.785 1.824
## [1] 0.01809216
## [1] "********"
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## [1] "one variable, logistic regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.981 1.984 1.986 1.986 1.988 1.991
## [1] 0.002469481
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 9"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5658 -0.9120 0.3055 0.8035 2.7112
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.68760 0.06161 11.16 <2e-16 ***
## x1 3.18452 0.23641 13.47 <2e-16 ***
## n1 2.45247 0.15572 15.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 1990.5 on 1997 degrees of freedom
## AIC: 1996.5
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"
## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"
## [1] "effects model, sigma= 9"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 9 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2017 -1.1260 0.4599 1.0784 1.9202
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.53470 0.05559 9.619 < 2e-16 ***
## x1 3.31977 0.21977 15.105 < 2e-16 ***
## n1 0.48966 0.08909 5.497 3.87e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2410.7 on 1997 degrees of freedom
## AIC: 2416.7
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable plus noise variable, logistic regression Noised 9 train mean deviance 1.73892471181772"
## [1] "one variable plus noise variable, logistic regression Noised 9 test mean deviance 1.79890921580638"
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2012 -1.1757 0.5026 1.1657 1.5936
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.42346 0.05493 7.710 1.26e-14 ***
## x1 3.00699 0.20534 14.644 < 2e-16 ***
## n1 -0.05278 0.02435 -2.167 0.0302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2455.4 on 1997 degrees of freedom
## AIC: 2461.4
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"
## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.747 1.768 1.775 1.775 1.781 1.815
## [1] 0.01165322
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.750 1.763 1.773 1.773 1.780 1.797
## [1] 0.01142931
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.074 3.464 3.597 3.603 3.717 4.144
## [1] 0.2241516
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.761 1.788 1.800 1.804 1.816 1.871
## [1] 0.02436318
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression ObliviousModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.979 1.984 1.986 1.986 1.988 1.994
## [1] 0.003256879
## [1] "********"
## [1] "*************************************************************"